The Role of a Cerebellum-Driven Perceptual Prediction within a Robotic Postural Task

Abstract

Postural adjustments are acquired compensatory and anticipatory motor responses maintaining balance and equilibrium against self-induced or external perturbations. It has been proposed that the cerebellum could be involved in issuing such predictive motor actions. However, it remains unclear what strategy is adopted by the brain in order to make such prediction and how anticipatory and compensatory components are integrated into a single response. Within this study we are interested in the computational mechanisms underlying the acquisition of anticipatory responses in a postural task. We compare two alternative architectures representing two different hypotheses: anticipation either as sensory-to-motor association or as sensory-to-sensory association. We propose to use a cerebellar model to control the acquisition of an adaptive motor response in a simulated robotic setup. We devise a scenario where a cart-pole robot is trained to predict a perturbation and issue an anticipatory action to minimize the disturbance on its state of equilibrium. Our results show that a cerebellum based architecture can efficiently learn to reduce errors through anticipation. We also suggest that a sensory-to-sensory prediction could be less expensive in terms of energy cost and more robust when events violate the acquired prediction.